Principal Disparity Estimators That Mitigate Measurement Biases

Oregon Convention Center 

We considered methods to assess counterfactual disparity of an endpoint outcome conditioned on principal strata of an intermediate variable that is prone to measurement errors and subsequent misclassification of the principal strata. The proposed method incorporated fairness algorithms (for risk adjustments) with Bolk-Croon-Hagenaars method to mitigate measurement bias. We considered 1-step and 3-step error-less ML estimators to derive 'principal disparity' under respective data-guided identification assumptions. Efficiency, consistency and utilities of the proposed estimators are compared.